A technology of extracting a semantic topic from a document is one of the most attention-attracting fields in recent years. In particular, as blogs or social network service sites gain popularity, a lot of research for automatically extracting opinions about an object in a text posted by a user on the Internet has been carried out.
However, according to a conventional method, it is impossible to automatically extract a topic including a sentiment-oriented ratable aspect of an object and a sentiment about the topic.
With regard to extracting a topic from a document, U.S. Patent Laid-open Publication No. 2012/0095952 (“COLLAPSED GIBBS SAMPLER FOR SPARSE TOPIC MODELS AND DISCRETE MATRIX FACTORIZATION”) discloses a configuration for extracting a topic from a document corpus by generating a Dirichlet probability distribution using LDA (Latent Dirichlet Allocation) and an IBP (Indian Buffet Process) and performing inference using a collapsed Gibbs sampling algorithm.
Further, U.S. Pat. No. 7,853,596 (“Mining geographic knowledge using a location aware topic mode”) discloses a configuration for extracting location information included in a document by generating a probability distribution using LDA (Latent Dirichlet Allocation) and performing inference using an EM (Expectation Maximization) algorithm.